continuous-agent-loop

Build a continuous autonomous agent loop by integrating RFC-style task decomposition, quality gates, and eval harnesses to sustain long-running AI workflows. This pattern maintains reliable execution through strict progress monitoring and modular composition.

The best way to handle recovery for autonomous agents is applying structured options like freezing the loop, targeted re-runs, and audits. This approach handles failures gracefully while maintaining loop state persistence.

Yes, you can use this pattern for production-grade AI workflows in Gemini CLI environments. It satisfies requirements for deterministic task coordination, loop state persistence, and guardrail integration within complex tasks.

Orchestrate safe failover in continuous loops by applying recovery controls and quality gates to maintain reliable execution. This pattern ensures strict progress monitoring and graceful failure handling across complex tasks.

You need session persistence for autonomous agent workflows to maintain reliable execution during long-running tasks. It supports modular pattern composition and ensures loop state is saved for scalable AI agent orchestration.